FAIR stands for Findable, Accessible, Interoperable, Re-usable principles (The FAIR principles as published by FORCE11). The FAIR Data principles act as an international guideline for high quality data stewardship. Throughout the FAIR Principles, we use the phrase ‘(meta)data’ in cases where the Principle should be applied to both metadata and data.

“Percentage of time spent finding and organising data according to research data specialists: 79%” (#RDAPlenary). Processing and curating data according to FAIR principles both save time and have impact through information and knowledge build upon this data.

In a move to sharp its focus on the processing data according to FAIR principles rationales, the Research Data Alliance, for example, keeps identifying different parameters of FAIRness helpful to establish nodes (e.g. ELIXIR nodes: the national implementation of a harmonised FAIR Data Management programmes) for FAIRifying data, important in maximizing the discovery and reusability of digital resources in long term goal.

FAIR Data Principles apply not only to data but also to metadata, and are supporting infrastructures (e.g., search engines). Most of the requirements for findability and accessibility can be achieved at the metadata level, but interoperability and reuse require more efforts at the data level. This scheme depicts the FAIRification process adopted by GO FAIR.

Even though Open Data and FAIR Data are different, they can be overlapping concepts; FAIR data doesn’t not automatically imply that it needs to be accessible - there can be limitations to access, for example, for sensitive data. Accessibility of FAIR data means “how-to-access” and is defined in a human- and machine-readable way.

FAIR data practices have arrived in the communities because they have been shown to increase the quality of scientific findings. Getting more FAIRdata and moving its trust forward is also not just about FAIR SCIENCE, it's about FAIR SOCIETY...

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"Implementing FAIR requires a model for #FAIRData Objects which have a PID linked to different types of essential metadata, including provenance and licencing. Use of community standards and sharing of code is fundamental for interoperability and reuse." (Twitter)

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The CODATA Data Science Journal is a peer-reviewed, open access, electronic journal, publishing papers on the management, dissemination, use and reuse of research data and databases across all research domains